ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
This research paper presents ForeseeDrive, a method that converts future scene evolution into an interface for shaping generative driving candidates. By deriving query-indexed tokens from logged motion and occupancy data to condition trajectory geometry before candidate generation, the approach aims to recover maneuvers absent in standard sampling menus. The system includes mechanisms like zero-token-gated residual paths and TTC-margin guards to limit drift and ensure safety. Benchmark results show an 89.2 PDMS score on NAVSIM navtest, 0.41 m average L2 error, and a 0.05% collision rate on nuScenes validation, alongside a 76.50 driving score on Bench2Drive. Diagnostics reveal mixed oracle-best quality and localized failures near safe-to-unsafe boundaries, supporting the utility of future-conditioned generation as a practical anticipation interface.
The paper introduces a generative driving planning method that uses future-token conditioning to shape trajectory candidates before they are generated, achieving specific benchmark scores on NAVSIM and nuScenes while identifying localized failures at TTC-boundary crossings.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
average L2 error
0.41
View original evidence
s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures
Open source S4driving score
76.5
Reported trials
View original evidence
s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures
Open source S4PDMS score
89.2
Reported trials
View original evidence
s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures
Open source S4
- code: Reported pending Open source S5
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures
Open source S4
ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
Open source S1
arXiv:2611.14286v1 Announce Type: new Abstract: Generative driving planners can represent diverse ego futures, but a downstream scorer can choose only among sampled candidates; it cannot recover an absent maneuver. We present ForeseeDrive, which turns future scene evolution into a deployable interface for shaping candidate generation. Rather than reconstructing dense future states or scoring a fixed menu, ForeseeDriv
Open source S2
e derives query-indexed tokens from logged actor motion, occupancy evolution, route interactions, and TTC margins, and predicts the same token space from present context at deployment. The tokens condition trajectory geometry before candidates materialize. A zero-token-gated residual path and candidate-preservation loss limit unnecessary drift, while a TTC-margin guard targets brittle safe-to-unsafe boundary crossing
Open source S3
. Together, the benchmark results and bounded mechanism tests support future-conditioned generation as a practical interface for anticipation. Code is included in the supplementary materials.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org